For AI, a real-world reality check

An intelligent computer is only as well-rounded as the people who teach it.

For the past three summers, around two dozen would-be
computer scientists have come to Stanford University to learn about
artificial intelligence from some of the field’s brightest. The attendees,
culled from hundreds of applicants, take day trips to nearby tech companies,
interact with social robots and hexacopters, and learn about computational
linguistics (what machines do when words have multiple meanings, say) and the
importance of time management (very). They play Frisbee. But if your mental
picture of AI is a bunch of guys creating wilier enemies for their favorite
videogames, well, this isn’t that. All the students here at the Stanford
Artificial Intelligence Laboratory’s Outreach Summer (SAILORS) program are girls who have just
completed ninth grade, and their studies focus on finding ways to improve
lives, not enhance their game play: How do we use AI to keep jumbo jets from
careening into one another? To ensure that doctors wash their hands before
hitting the OR? “Our goal was to rethink AI education in a way that
encourages diversity and students from all walks of life,” says Fei-Fei Li,
director of Stanford’s AI lab and a founder of the SAILORS program. “When you
have a diverse range of future technologists, they really care that
technology is being used for the good of humanity.”

“When you have a diverse range of future technologists, they
really care that technology is being used for the good of
humanity.”

—Fei-Fei Li,
Google & Stanford

SAILORS was created in 2015 by Li and former student Olga Russakovsky (now an
assistant professor at Princeton University) to help bring greater gender
equality to the tech industry. The cause is both noble and urgent. According
to a recent survey, the number of women seeking computer science degrees is
dropping; in the AI sector, women hold less than 20 percent of executive
positions. It’s an enormous field to be left out of, considering that, every
day, more and more people use AI to make their lives easier and more
efficient: AI is how photo apps recognize your face among everyone else's,
not to mention the beach where you took the picture. It's how your devices
understand you when you ask what the weather will be tomorrow. Then there are
the lesser-known applications, like diagnosing diabetic retinopathy (which
often leads to blindness) or sending a drone on a search-and-rescue mission
to the most remote reaches of the world.

With AI becoming ever more ubiquitous, the need for gender balance in the
field grows beyond just the rightness of the cause—diversity is a crucial
piece of AI due to the nature of machine learning. A goal of AI is to prod
machines to complete tasks that humans do naturally: recognize speech, make
decisions, tell the difference between a burrito and an enchilada. To do
this, machines are fed vast amounts of information—often millions of words or
conversations or images—just as all of us absorb information, every waking
moment, from birth (in essence, this is machine learning). The more cars a
machine sees, the more adept it is at identifying them. But if those data
sets are limited or biased (if researchers don’t include, say, images of
Trabants), or if the folks in AI don’t see or account for those limits or
biases (maybe they’re not connoisseurs of obscure East German automobiles),
the machines and the output will be flawed. It’s already happening. In one
case, image recognition software identified photographs of Asian people as
blinking.

“It’s not just about having transparency in data. We actually
need to make the numbers move in the right direction.”

—Tracy Chou,
Project Include

How do humans create more inclusive labs and workspaces? A number of projects
and individuals are taking on that challenge. This year, Li—who is also chief
scientist of AI and machine learning at Google Cloud—and others helped launch
AI4ALL. The national nonprofit is aimed at
bringing greater diversity to AI and has engaged experts in genomics,
robotics, and sustainability as mentors. It’s building on the work of SAILORS
but also targeting people of color and low-income students across the country
through partnerships with Princeton, UC Berkeley, and Carnegie Mellon, in
addition to Stanford. “We had a lot of colleagues and industry leaders coming
up to us and saying, ‘SAILORS is great, but it’s just Stanford serving a few
dozen students per year, mostly from the Bay Area,’ ” Li says. “So AI4ALL is
about diversity and inclusion. It’s not only gender.”

AI and ML

What's the difference?

The terms artificial intelligence (AI) and machine learning (ML) are often used
interchangeably, but they’re not the same thing. AI describes machines’ ability
to seemingly mimic human ways of thinking, learning as they go as opposed to
following specific commands. ML is one of the most efficient—and
popular—techniques that computers employ to gain that ability. In ML, machines
sift through examples to recognize patterns.

Other similar initiatives include
Code Next, Google’s Oakland-based effort to encourage Latino and African
American students to explore careers in tech; DIY Girls, an educational and mentoring STEAM
(science, technology, engineering, art, and math) program for under-resourced
communities in Los Angeles; and Project
Include, which helps new and midstage startups hire more women and people
of color. Tracy Chou, formerly of Pinterest, founded Project Include last
year with seven other prominent women in the tech industry. In 2013, Chou
famously urged tech companies to come clean about how many women they
employed. As the numbers trickled in, they substantiated what everyone in
Silicon Valley knew: The tech world, from the biggest corporation to the
smallest startup, is overwhelmingly white and male. Project Include, says
Chou, was the logical next step. “After a couple of years of these data
reports coming out and not a lot of change happening, there started to be a
shift in the conversation,” she says. “Now it’s not just about having
transparency in data. We actually need to make the numbers move in the right
direction.”

That direction includes making work in the field of AI more accessible to the
masses. There are relatively few people employed in AI, and already we’re
seeing robots that care for people and personal assistants that anticipate
our needs. With humans controlling the data and criteria and machines doing
the work, better and greater human input means better and greater results.

In many ways, the democratization of AI is already on its
way. Take this example: In Japan, a farmer’s son used AI to sort his family’s
harvest of cucumbers by various characteristics. It’s the kind of story that
appeals to Li, who came to the US from China at age 16 knowing little about
her adopted country and even less about New Jersey, where she ended up. After
working a variety of odd jobs, from cleaning houses to walking dogs to
cashiering at a Chinese restaurant, Li found herself at Princeton, and later
at graduate school at Caltech.

Li comes to her work as a triple outsider: an immigrant, a woman, and a
person of color in a world dominated by white men. What might have been
obstacles for anyone else have become prods for Li. She spends much of her
time studying computer vision, a component of machine learning she calls “the
killer app of AI.” Computer vision analyzes and identifies visual data and
may someday help create more responsive robotic limbs, say, or solve the
knottiest of mathematical proofs. But as with all AI, the key to this
technology is teaching machines to unpack a wealth of information from
different places and perspectives. To be, in essence, visual citizens of the
world—not unlike Li.

Fostering a diverse group of creators to shape that world is essential to the
sorts of story and technical issues that content strategist Diana Williams
encounters every day at ILMxLAB, the top-secret Lucasfilm dream center where
developers craft immersive, interactive entertainment—a VR encounter with
Darth Vader, perhaps—inspired by the vast Star Wars universe. Williams is
deeply involved in pro-tech organizations like Black Girls Code and remembers the
dearth of women of color at her college in the ’80s. “I was always the only
one in my math classes, the only one in my business classes,” she says. “That
gets tiring, and it gets scary.” Her solution to pointing more women toward
tech: “Start them young and get them strong in their confidence, so that when
they walk into the room and they’re the only ones there, they don’t turn
around.”

“Start them young and get them strong in their confidence, so
that when they walk into the room and they’re the only ones there, they
don’t turn around.”

—Diana Williams,
Lucasfilm

Maya Gupta, a machine-learning researcher at Google, is working to improve
AI, albeit from a different angle. At Stanford, she helped a Norwegian
company detect cracks in its underwater gas pipelines. “You can’t go in there
very well, so we had to use partial information to try to guess,” she says.
Teaching machines to make nuanced guesses is familiar terrain to Gupta. If
you’re on YouTube listening to tenor saxophonist Kamasi Washington’s “Truth”
and the music effortlessly segues into Alice Coltrane’s gorgeous “Turiya and
Ramakrishna,” like the work of the smartest DJ you never knew, thank Gupta,
whose team helps computers fine-tune their recommendations. “It’s all about
predicting, right?” she says. “You’re trying to guess what’s going on with
limited data.”

Today she’s leading a research and development team at Google to, among other
things, create greater accuracy in machine learning. “Let’s say I want to be
equally accurate at identifying a Boston accent and a Texas accent, but I
have a speech recognizer that’s a little better at the Texas one,” she says.
“Should I penalize the people with a Texas accent by making the recognition
just as bad as it is for Boston, to be fair? And what if it’s simply harder
to recognize people speaking with a Boston accent?”

Gupta and her team are also refining systems that would be infinitely more
transparent than their carbon-based designers. With machines, the hope goes,
we can eliminate many of the biases or subconscious processes that plague
human thought—or at least more easily recognize them when they emerge.
Machines don’t lose focus when they’re tired, or irritable, or hungry. A
study showed that judges are less apt to grant parole right before lunch,
when they’re thinking of sandwiches rather than sidebars. “It’s hard to
measure what’s really going on in the minds of humans,” Gupta says. “We want
our machine-learning systems to be explainable, and frankly many of them are
already more explainable than humans are.”

“We want our machine learning systems to be explainable, and
frankly many of them are already more explainable than humans
are.”

—Maya Gupta,
Google

As AI becomes increasingly useful—not to mention easier to use—the push is on
to place it into as many hands as possible. Christine Robson, an IBM
researcher before coming to Google, is an enthusiastic champion of open
source software like TensorFlow, a machine-learning system that can be used
for a host of tasks, from translating languages to spotting illnesses to
creating original art.

For Robson, inclusivity in AI means making its tools accessible to more than
just self-professed math nerds like herself. “I’m excited about the
availability of machine learning to the world,” she says. “We talk a lot
about democratizing machine learning, but I am a big believer in this. Making
these tools really easy to use, and making these techniques possible for
everybody to apply, is just so critical.”

Sci-fi literature and film have long proffered examples of
AI gone awry (Mary Shelley’s Frankenstein turns 200 next year).
Today, many in the industry—including Li, Robson, and Chou—are concerned less
about what AI might do to us and more about what we humans might do to AI. An
example: Programmers give virtual assistants a female voice because, well,
men and women alike tend to prefer one. “But it perpetuates this idea that
assistants are female, so when we engage with these systems, it reinforces
that social bias,” says Chou. Many of the field’s best minds worry about
what’s going into real-life AI systems—and thus what’s going to emerge.
That’s where the push for greater diversity in AI comes in. Little of this
will be easy. But its proponents are smart, resourceful, and committed to the
cause.

“Making these AI tools really easy to use, and making these
techniques possible for everyone to apply, is just so
critical.”

—Christine Robson,
Google

We have to make sure that everyone feels welcome, Gupta says. She recalls the
wall of photographs of retired electrical-engineering professors at her alma
mater Rice that ‘’did not look like me.” We need to convince girls that AI
isn't magic, adds Robson. “It's math."

At SAILORS, students are learning how to use natural language processing to
search social media and aid in disaster relief. “It would help rescuers
discover people in need in real time, using their Twitter messages,” Li says.
The effects of the classes and projects last well past the unforgettable
summers. Some of the students have started their own robotics clubs at
school, published pieces in scientific journals, and held workshops at middle
schools to spread the gospel of AI to even younger girls. For these students,
whose backgrounds and experiences are as diverse as the myriad projects they
tackled at camp, AI isn’t the latest cool gadget, but a powerful force for
good. In the lead-up to the first SAILORS gathering in 2015, the program
shared messages from incoming campers, including this ambitious wish: “I hope
to begin my AI journey now so I can make an impact on the world in the
future.”

Robert Ito is a writer based in Los Angeles. He is a frequent
contributor to the New York Times, Salon, and Los
Angeles magazine.